MATLAB and Simulink for Automated Driving Systems

Automotive engineers use MATLAB® and Simulink® to design automated driving system functionality including computer vision, path planning, and sensor fusion and controls development. With MATLAB and Simulink, you can:

“MATLAB is my preferred tool because it speeds algorithm design and improvement. I can do the data analysis, algorithm development, algorithm visualization, and simulation in one place and then generate C code that is reliable, efficient, and easy for software engineers to integrate within a larger system.”

Perception System Design

With MATLAB, you can use prebuilt algorithms and sensor models for computer vision, lidar processing, radar, and sensor fusion. Perform sensor fusion using a library of tracking and data association techniques including point and extended object trackers. Simulate measurements from inertial and GPS sensors, and design fusion and localization algorithms to estimate vehicle position and orientation.

Simulation-Based Testing

Start testing your automated driving algorithms using the Driving Scenario Designer app, which lets you build scenarios or load prebuilt ones-- including EuroNCAP. Generate detections from your statistical radar and camera models and analyze the output in MATLAB or Simulink.

You can also use the 3D environment provided with the reference applications to develop your own virtual test ground for ADAS and automated driving features. For example, the vehicle models come with a virtual camera that sends images back to Simulink during the simulation. You can analyze the signals in Simulink to test your lane detection algorithm. Customizing the scenes in the Unreal Engine editors gives you additional flexibility to create and simulate scenarios that fully exercise your ADAS and automated driving features.